This article is part of a series on Methods for Health Surveys in Difficult Settings, edited by Oleg Bilukha (Centers for Disease Control), Kristof Bostoen (London School of Hygiene and Tropical Medicine), Francesco Checchi (London School of Hygiene and Tropical Medicine), Bridget Fenn (London School of Hygiene and Tropical Medicine), Oliver Morgan (London School of Hygiene and Tropical Medicine) and Anne-Marie ter Veen (London School of Hygiene and Tropical Medicine).

Analytic perspective

Optimisation of the T-square sampling method to estimate population sizes

Kristof Bostoen¹ , Zaid Chalabi² and Rebecca F Grais³

¹  Department of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK

²  Department of Public Health and Policy, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK

³  Epicentre, 8 rue Saint Sabin, 75011 Paris, France

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Emerging Themes in Epidemiology 2007, 4:7doi:10.1186/1742-7622-4-7

Published:	1 June 2007

Abstract

Population size and density estimates are needed to plan resource requirements and plan health related interventions. Sampling frames are not always available necessitating surveys using non-standard household sampling methods. These surveys are time-consuming, difficult to validate, and their implementation could be optimised. Here, we discuss an example of an optimisation procedure for rapid population estimation using T-Square sampling which has been used recently to estimate population sizes in emergencies. A two-stage process was proposed to optimise the T-Square method wherein the first stage optimises the sample size and the second stage optimises the pathway connecting the sampling points. The proposed procedure yields an optimal solution if the distribution of households is described by a spatially homogeneous Poisson process and can be sub-optimal otherwise. This research provides the first step in exploring how optimisation techniques could be applied to survey designs thereby providing more timely and accurate information for planning interventions.